In modern astronomy, the large amount of raw data produced by the newest surveys is far beyond our traditional processing capacity, and this insufficient computing power has become the bottleneck to the rapid development of astrophysics. Fortunately, the development of computer science has provided us with new approaches to understanding and gaining knowledge from these raw data. Especially, machine learning has achieved remarkable success in offering novel solutions to solve complex problems, based on applications of loss functions, optimization methods [1]}, and statistical models [2]}. Owing to its non-functional algorithms [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, machine learning can reveal potential patterns and important parameters that are indistinguishable by traditional scientific or statistical methods.
